A physician whose patient has symptoms of a bacterial infection faces a difficult choice - use an antibiotic that might not work or even be needed, or wait, sometimes days, to have verification of bacterial infection from the microbiology laboratory.

The physician typically gives the patient an antibiotic in hopes it will kill the organism, but knows that there is a chance that the bacteria is resistant to the drug. A faster way to identify antibiotic resistance is desperately needed.

In a report that appears online in the journal PLOS ONE, researchers from Baylor College of Medicine describe a technique of pooling drug-resistant bacteria (Escherichia coli) with similar antibiotic resistance and sequencing the DNA of those pools. Looking for a set of mutations as a "read-out" for resistance to a family of antibiotics called fluoroquinolones (for example, ciprofloxacin or Cipro®), they identified mutations in four genes (gyrA, mutM, ligB and recG) consistently present in all pools of microbes that were resistant to fluoroquinolones. Indeed, these four mutations were conserved in all the fluoroquinolone-resistant isolates banked in the laboratory since 1999. This approach could be used to uncover conserved mutations in bugs resistant to other drugs and to other problematic bacteria as well.

"We wanted to understand what genes are changing to make these bacteria resistant to drugs and to give clinicians a way to determine quickly, by measuring just these changes - a process that should take hours as opposed to days, whether the bacterial infection is resistant to drugs - before they give them," said Dr. Lynn Zechiedrich, professor of molecular virology and microbiology at BCM and corresponding author of the report.

The issue of drug-resistance and particularly fluoroquinolone resistance has been an important part of Zechiedrich’s research. Over the years, her laboratory has collected thousands of samples with the notion of one day sequencing them. After long and careful consideration of the problem, her graduate student, Dr. Michelle Swick (now of The University of Texas Medical School at Houston), collaborator Dr. Richard Sucgang, assistant professor of biochemistry and molecular biology, and she chose 164 strains of bacteria that represented all of the antibiotic resistance in the entire collection and designed the pooling/sequencing approach. Sequencing each bacterium one-by-one and then hoping that the antibiotic resistance changes would somehow emerge was not going to work, at least not quickly.

"So, we decided to ignore all the other information about the bacterium, for example which body site the bacteria came from, how virulent it was, which ward or hospital it came from, which patient and what age or gender the patient was. We put only the antibiotic resistance information into the computer for every strain we had and let a computer algorithm determine how the bacteria clustered considering only resistance status to 21 different antibiotics," she said.

"We saw that the computer grouped the microbes by antibiotic resistance beautifully. For example, in one pool, all the bacteria were resistant to 12 drugs and no others. The mutations in the genome that we see that are the same across all microbes in the pool are associated with the antibiotic resistance of that pool. The ones that are not consistent could have to do with the myriad other differences among the different bacteria," she said.

This pooling approach simplifies the identification of known gene variants involved in antibiotic resistance. And in this case, the environment was implicated as a source of antibiotic resistance genes, and the technique identified strong similarity to a strain of E. coli known as SMS-3-5 that came from an industrial, toxic metal-contaminated coastal environment and the pools of multidrug-resistant E. coli that had the greatest resistance to the fluoroquinolones.

"What's next is to design a rapid detection test for the mutations that correlate with antibiotic resistance so that health care providers can quickly determine not only the presence of bacteria causing the infection (as opposed to viruses) but also the antibiotic resistance status of those bacteria. This kind of system would minimize the empirical use of antibiotics to treat bacterial infections," said Zechiedrich, "while increasing treatment efficiency and extending the useful life of the antibiotics we have now."

Others who took part in this work include Michael A. Evangelista, Truston J. Bodine, Jeremy R. Easton-Marks, Patrick Barth, Richard J. Hamill, David Steffen, Lauren B. Becnel, all of BCM, and Minita J. Shah, Sarah Stanley, Stephen F. McLaughlin, Clarence C. Lee and Vrunda Sheth, all of Life Technologies in Beverly, Mass.; and Christina A. Bormann Chung and Quynh Doan, both of Life Technologies in Foster City, Calif.

Funding for this work came from the NIH (RO1AI054830) and from a sequencing award from the Applied Biosystems SOLiDTM System $10K Genome Grant Program.

Zechiedrich holds the Kyle and Josephine Morrow Chair in Molecular Virology and Microbiology, and is also a professor of in the departments of biochemistry and molecular biology, and pharmacology.